Various vehicles or work machines, such as trucks, dozers, motor graders, wheel loaders, wheel tractor scrapers, and the like, are often used to simultaneously perform a variety of different tasks within a given worksite. For larger projects extending over longer periods of time, it can be useful to track the progress of the work being performed, which involves not only monitoring the efficiency with which each of the work machines is being operated, but also tracking overall work productivity. One manner of tracking work progress relies on combinations of sensors and tracking devices that are installed on the work machines and within the worksite. Specifically, the sensors track operations performed by each individual work machine, while the tracking devices track the locations of the work machines relative to the worksite as well as changes in the terrain within the worksite. Although such conventional tracking methods may serve its purpose, there is still room for improvement.
In light of modern technological advancements, the conventional method of monitoring feedback from sensors and tracking devices can appear overly complex and tedious. For instance, sensors and tracking devices can take up a considerable amount of physical space on a work machine or within a worksite. The installation, configuration, and maintenance of such sensors and tracking devices can also be significant in terms of both cost and time, especially in larger projects or worksites which may require several sets of work machines to operate simultaneously at any given moment. Furthermore, the feedback provided by sensors and tracking devices are also not exempt from errors and miscommunications, which can be costly and time-consuming to detect and correct. Thus, there is a general need for a more simplified tracking technique that is less intrusive to the work machines and the worksite.
The use of visual recognition systems is becoming increasingly more widespread. In general, visual recognition systems are used to identify different objects within a digital image or video. One such visual recognition system is disclosed in U.S. Pat. No. 8,503,760 (“Lee”). Lee identifies a location and pose of an object within an image using visual recognition techniques. Lee then estimates changes in the object position or pose using a combination of probabilistic modeling and filtering techniques. Although Lee may be adequate for objects with limited movement or for applications which allow for estimation errors, the techniques in Lee may be inadequate for distinguishing between and tracking various work machines within a dynamically changing environment, where each machine has several moving components and possibly even articulation, and where work productivity analyses rely on more accurate feedback.
In view of the foregoing disadvantages associated with conventional monitoring or tracking techniques, a need exists for a solution which is not only capable of effectively tracking multiple vehicles or work machines within a changing environment, but also capable of doing so less intrusively. In particular, there is a need for a monitoring system that relies less on sensors and tracking devices and more on visual recognition techniques not only to classify different work machines from captured images, but also to track the operations of the individual work machines. The present disclosure is directed at addressing one or more of the deficiencies and disadvantages set forth above. However, it should be appreciated that the solution of any particular problem is not a limitation on the scope of this disclosure or of the attached claims except to the extent expressly noted.